東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Material Failure Simulation with Random Microstructure Using Lattice Particle Method and Neural Network.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Material Failure Simulation with Random Microstructure Using Lattice Particle Method and Neural Network./
Author:	Wei, Haoyang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	163 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-11, Section: B.
Contained By:	Dissertations Abstracts International82-11B.
Subject:	Mechanics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28492218
ISBN:	9798738623189

Material Failure Simulation with Random Microstructure Using Lattice Particle Method and Neural Network.
Wei, Haoyang.

Material Failure Simulation with Random Microstructure Using Lattice Particle Method and Neural Network. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 163 p.

Source: Dissertations Abstracts International, Volume: 82-11, Section: B.

Thesis (Ph.D.)--Arizona State University, 2021.

This item must not be sold to any third party vendors.

Extensive efforts have been devoted to understanding material failure in the last several decades. A suitable numerical method and specific failure criteria are required for failure simulation. The finite element method (FEM) is the most widely used approach for material mechanical modelling. Since FEM is based on partial differential equations, it is hard to solve problems involving spatial discontinuities, such as fracture and material interface. Due to their intrinsic characteristics of integro-differential governing equations, discontinuous approaches are more suitable for problems involving spatial discontinuities, such as lattice spring method, discrete element method, and peridynamics. A recently proposed lattice particle method is shown to have no restriction of Poisson's ratio, which is very common in discontinuous methods. In this study, the lattice particle method is adopted to study failure problems. In addition of numerical method, failure criterion is essential for failure simulations. In this study, multiaxial fatigue failure is investigated and then applied to the adopted method. Another critical issue of failure simulation is that the simulation process is time-consuming. To reduce computational cost, the lattice particle method can be partly replaced by neural network model.First, the development of a nonlocal maximum distortion energy criterion in the framework of a Lattice Particle Model (LPM) is presented for modeling of elastoplastic materials. The basic idea is to decompose the energy of a discrete material point into dilatational and distortional components, and plastic yielding of bonds associated with this material point is assumed to occur only when the distortional component reaches a critical value. Then, two multiaxial fatigue models are proposed for random loading and biaxial tension-tension loading, respectively. Following this, fatigue cracking in homogeneous and composite materials is studied using the lattice particle method and the proposed multiaxial fatigue model. Bi-phase material fatigue crack simulation is performed. Next, an integration of an efficient deep learning model and the lattice particle method is presented to predict fracture pattern for arbitrary microstructure and loading conditions. With this integration, computational accuracy and efficiency are both considered. Finally, some conclusion and discussion based on this study are drawn.

ISBN: 9798738623189Subjects--Topical Terms:

525881
Mechanics.
Subjects--Index Terms:

Elastoplasticity

Material Failure Simulation with Random Microstructure Using Lattice Particle Method and Neural Network.
LDR:03639nmm a2200373 4500 001 2342183
005 20211209144704.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798738623189
035 $a (MiAaPQ)AAI28492218
035 $a AAI28492218
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wei, Haoyang. $3 3680495
245 1 0 $a Material Failure Simulation with Random Microstructure Using Lattice Particle Method and Neural Network.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 163 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-11, Section: B.
500 $a Advisor: Liu, Yongming.
502 $a Thesis (Ph.D.)--Arizona State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Extensive efforts have been devoted to understanding material failure in the last several decades. A suitable numerical method and specific failure criteria are required for failure simulation. The finite element method (FEM) is the most widely used approach for material mechanical modelling. Since FEM is based on partial differential equations, it is hard to solve problems involving spatial discontinuities, such as fracture and material interface. Due to their intrinsic characteristics of integro-differential governing equations, discontinuous approaches are more suitable for problems involving spatial discontinuities, such as lattice spring method, discrete element method, and peridynamics. A recently proposed lattice particle method is shown to have no restriction of Poisson's ratio, which is very common in discontinuous methods. In this study, the lattice particle method is adopted to study failure problems. In addition of numerical method, failure criterion is essential for failure simulations. In this study, multiaxial fatigue failure is investigated and then applied to the adopted method. Another critical issue of failure simulation is that the simulation process is time-consuming. To reduce computational cost, the lattice particle method can be partly replaced by neural network model.First, the development of a nonlocal maximum distortion energy criterion in the framework of a Lattice Particle Model (LPM) is presented for modeling of elastoplastic materials. The basic idea is to decompose the energy of a discrete material point into dilatational and distortional components, and plastic yielding of bonds associated with this material point is assumed to occur only when the distortional component reaches a critical value. Then, two multiaxial fatigue models are proposed for random loading and biaxial tension-tension loading, respectively. Following this, fatigue cracking in homogeneous and composite materials is studied using the lattice particle method and the proposed multiaxial fatigue model. Bi-phase material fatigue crack simulation is performed. Next, an integration of an efficient deep learning model and the lattice particle method is presented to predict fracture pattern for arbitrary microstructure and loading conditions. With this integration, computational accuracy and efficiency are both considered. Finally, some conclusion and discussion based on this study are drawn.
590 $a School code: 0010.
650 4 $a Mechanics. $3 525881
650 4 $a Materials science. $3 543314
653 $a Elastoplasticity
653 $a Fracture
653 $a Lattice particle method
653 $a Material failure
653 $a Multiaxial fatigue
653 $a Random loading fatigue
690 $a 0346
690 $a 0794
710 2 $a Arizona State University. $b Mechanical Engineering. $3 1675155
773 0 $t Dissertations Abstracts International $g 82-11B.
790 $a 0010
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28492218